Graph theory for feature extraction and classification: a migraine pathology case study

Vista sencilla de ítem
dc.contributor.authorJorge Hernández, Fernando
dc.contributor.authorGarcía Chimeno, Yolanda
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorCabrera-Zubizarreta, Alberto
dc.contributor.authorGómez Beldarrain, María Ángeles
dc.contributor.authorFernández-Ruanova, Begoña
dc.date.accessioned2026-09-21T17:38:38Z
dc.date.available2026-09-21T17:38:38Z
dc.date.issued2014-11-01
dc.date.updated2026-09-21T17:38:38Z
dc.descriptionPonencia presentada en la 3rd International Conference on Biomedical Engineering and Biotechnology, celebrada en Beijing, China entre el 25 y el 28 de septiembre de 2014en
dc.description.abstractGraph theory is also widely used as a representational form and characterization of brain connectivity network, as is machine learning for classifying groups depending on the features extracted from images. Many of these studies use different techniques, such as preprocessing, correlations, features or algorithms. This paper proposes an automatic tool to perform a standard process using images of the Magnetic Resonance Imaging (MRI) machine. The process includes pre-processing, building the graph per subject with different correlations, atlas, relevant feature extraction according to the literature, and finally providing a set of machine learning algorithms which can produce analyzable results for physicians or specialists. In order to verify the process, a set of images from prescription drug abusers and patients with migraine have been used. In this way, the proper functioning of the tool has been proved, providing results of 87% and 92% of success depending on the classifier used.en
dc.description.sponsorshipThis publication has been funded by eVIDA research group grant from Education and Research de-partment of the Basque Country, Deiker from University of Deusto. Additionally, this publication is apart of the Health Research Projects of Ministry of Science and Innovation in Spain titled MIGREIN(PI11/01243)en
dc.identifier.citationJorge-Hernandez, F., Chimeno, Y. G., Garcia-Zapirain, B., Zubizarreta, A. C., Beldarrain, M. A. G., & Fernandez-Ruanova, B. (2014). Graph theory for feature extraction and classification: a migraine pathology case study. Bio-Medical Materials and Engineering, 24(6), 2979-2986. https://doi.org/10.3233/BME-141118
dc.identifier.doi10.3233/BME-141118
dc.identifier.eissn1878-3619
dc.identifier.issn0959-2989
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6670
dc.language.isoeng
dc.publisherIOS Press
dc.rights© 2014 – IOS Press and the authors
dc.subject.otherFunctional MRI (fMRI)
dc.subject.otherGraph theory
dc.subject.otherMachine learning
dc.subject.otherMigraine
dc.subject.otherSynchronization likelihood
dc.titleGraph theory for feature extraction and classification: a migraine pathology case studyen
dc.typeconference paper
dcterms.accessRightsopen access
oaire.citation.endPage2986
oaire.citation.issue6
oaire.citation.startPage2979
oaire.citation.titleBio-Medical Materials and Engineering
oaire.citation.volume24
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
jorge_graph_2014.pdf
Tamaño:
356.93 KB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Ponencias